Detecting Chasms and Cracks Using Innovator Scores and Agent Interactions

Abstract

In chasm theory, it is found from field data that many new products have an initial sales peak followed by a decline. In some cases, this decline lasts for a long period of time, which is named a chasm or crack. In this study, we model the phenomenon using innovator scores and agent-based modelling to understand the factors that cause it. We then conduct a sensitivity analysis of the exogenous variables that determine the behavior of the model. Specifically, we use innovator scores to classify users into innovator theory groups, and build an agent-based model. This study evaluates how cluster connectivity, which represents the word-of-mouth effect between each group, and product recognition range, which represents the advertising effect, affect the chasm or crack phenomenon and new product diffusion. Four scenarios are analyzed with different cluster connectivity and product recognition ranges. Additionally, for each scenario, we perform simulations that consider the interactions between agents and add considerations for new product diffusion measures. Evaluating this model using the behavioral and questionnaire data collected from users of an Online-to-Offline site, it is found that the parameters related to communication in the clusters are factors that cause the occurrence of chasms and cracks.